This invention relates generally to generation of a personalized dictionary for portable devices. More particularly, the present invention relates to a method for populating a personalized dictionary in a semi automated fashion. This is achieved through the analysis of communication messages written, spoken, sent or received on the portable device. Text may include any written characters, or transcriptions of verbal messages. Such text or verbal message may include text using Roman based alphabets, Chinese alphabet, Arabic scripts, or virtually any known language's symbology.
In today's increasingly mobile population, the ability to input text into a mobile device is becoming more desirable. Emails, appointments and text messages are routinely inputted into mobile devices, including Personal Digital Assistants (PDA's), cell phones and computerized organizers.
For the business person, the ability to send emails and document appointments, while on the go, enables a jumpstart into the workday, increased productivity and enhanced flexibility. For the teenage, or other casual user, text messaging has become an exceedingly common phenomena and a form of social currency.
Mobile devices typically have less processing power and storage resources available than a stationary computer system. Additionally, due to the small size of these personal appliances, keypads are typically very small or require multiple keytaps. This small, highly portable size of the devices that enable mobile text connectivity also render the input of such text onerous.
In response, typical personal portable appliances may include utilities that facilitate the generation or entry of textual material for messaging purposes. In general, these utilities may be one of several types, or some combination, including: i) systems which allow a user to enter text letter by letter using a scheme where a letter on a key is specifically identified in a deterministic fashion commonly called multi-tap systems, and ii) systems which match sequences of keys to word possibilities either algorithmically or by matching pre-stored dictionary entries, and iii) fully deterministic systems having a one to one correspondence to desired symbols such as a full keyboard, albeit miniaturized. These latter systems, of course, allow complete flexibility of symbol string entry.
In all of these systems, considerable benefit may be realized by providing the user with candidate words for selection by the user prior to completion. Particularly for long words, this predictive presentation of candidate words may save the user considerable typing time and keystrokes. Ordered dictionaries may be used to supply candidates and, given a well populated dictionary, results can be very good for many applications.
As noted, result quality is a strong function of the dictionary ordering strategy, so considerable effort is required to tune system performance so that the user experience is satisfactory. Poor candidates are a distraction rather than a benefit for the user, thus well populated dictionaries are a virtual necessity.
However, due to storage limitations in these portable devices, the dictionaries relied upon are necessarily not exhaustive word lists. Additionally, even were one able to have an exhaustive dictionary, querying such a database would be impractical for real time word candidate prediction, particularly for personal devices with limited processing ability.
As such, in typical systems, there are three essential components to the dictionary. The first is a static dictionary which is formulated from a substantial corpus in the target language. Such static dictionaries may additionally be referred to as a static element, base dictionary, first dictionary or static word list. In the initial use of the appliance, the performance of the utility is dominated by this static element. Although such a static dictionary may be changed in some modern appliances, such static dictionaries are, at best, quasi-static since changing content may confuse or distract the user and may confound manufacturer support activities.
The second dictionary component is a used word listing that may have an associated ordering algorithm. Such a used word list may additionally be referred to as a used word dictionary, usage dictionary, second dictionary or common word list. Whenever a user creates a message, words used in message creation are added to a dictionary that stores used words. This used word dictionary is helpful in that words and text constructs peculiar to that user are saved. Since a user tends, by and large, to use words and structures that have become habitual, and thus personal to the user, intended words may be predicted based upon the usage patterns established. This is believed to speed system response, generally, since users tend to re-use certain words and it is far better to keep a separate entry list than to attempt to manage the full dictionary; again system support is eased if the primary dictionary is kept fairly static.
A third list may be present that allows a user to create words that may be absent from the primary dictionary. Such a third list may additionally be referred to as a supplemental element, supplemental dictionary, third dictionary or supplemental word list. The supplemental dictionary allows preservation of the root dictionary whilst permitting a personal list of items, such as proper names or terms of art, relevant to a particular user to be stored.
Currently the population of the used word list and supplemental dictionary may require the user to input many words in full. That is, the user may be required to type in an entire word, often requiring the user to switch input modes to a deterministic input. Switching input modes may inconvenience the user, slow down messaging, and generally reduce efficiency and usability of the portable device. This inconvenience additionally occurs at a time when the dictionaries are sparsely populated, thus rendering generation of predictive candidates words limited, or worse, erroneous.
Another current method of addressing such an issue is to attempt to preload dictionary sets so that the user has fewer words to manually input. This has been met with mixed success, since such predetermined lists are very costly and difficult to compile, and are often non-reflective of what terms and words the user desires to use.
Thus, in the typical mobile device, the current lack of rapid dictionary population may be inadequate as requiring too much manual attention from the users, or requiring too much storage for exhaustive dictionary sets. Manufacturers and retailers of mobile devices would benefit greatly from the ability to offer devices with accurate and rapid dictionary word population. Additionally, users of these mobile devices would benefit greatly by having reduced aggravation and more efficiency when initially inputting text on the mobile device.
The current invention aids in automating, at least in part, the creation of the supplemental dictionary. A considerable benefit is that caller name records may be built rapidly as may be terms of art, thus freeing the user from the laborious task of creating each entry one by one.
It is therefore apparent that an urgent need exists for an improved system and method for automated dictionary population that is both accurate and efficient. This solution would replace current practices of making the user deterministically input each unknown word with a more efficient and rapid system with regards to mobile devices; thereby increasing effectiveness and general usability of text input performed on a mobile device.